Seizing the value of the surge: How to use M&A to reshape your IT organization

M&A is an opportunity to align technology functions to strategic focus areas by pushing internal resources and top talent toward business-facing, differentiated activities while aligning commodity activities toward key strategic partners.

In an earlier post, we explored the four dimensions of the IT workforce to assess to optimize merger and acquisition (M&A) success. As we further evaluate the IT workforce implications of M&A activity, it is notable that there is a tremendous (often underutilized) opportunity to leverage the sharp increase of work from M&A deals to redefine the IT workforce and organization. Leaders can better align technology functions to strategic focus areas by pushing internal resources and top talent toward business-facing, differentiated activities while aligning commodity activities toward key strategic partners that can manage those services even after the M&A activity has ended. Certainly, a like-for-like transformation makes sense in some cases, but there are four key areas to target to seize the value of the surge of demand that comes along with M&A integration or divestitures:

  1. Optimize location and sourcing strategy: M&A and divestiture transactions bring tremendous complexity and demand to IT organizations and, often, these organizations are not optimized from a labor supply and location perspective to be able to ramp up and down specific services based on the business needs throughout the life cycle of integration or divestiture. M&A is an opportunity for IT leaders to create a mandate around strategic consolidation of providers and moving resources to the right locations based on economics, skills and other organizational factors. The use of M&A as a lever creates the momentum to shift delivery methods and capabilities through strategic supplier selection. For example, organizations can set the stage to move from traditional waterfall approaches into agile and DevOps. It might not happen immediately, but major transformations such as an integration can pave the way. For IT leaders seeking a push toward new platforms or the cloud, selecting and utilizing providers with these types of emerging capabilities is the right move. Additionally, leaders can seize on high-priority initiatives across the IT organization that will maximize capital efficiency and minimize run/support activities in the long term by taking advantage of optimized sourcing approaches.
  2. Leverage opportunities for robotics and artificial intelligence: M&A is always a time to welcome new colleagues to the workforce — this time, they are going to include bots and machine learning algorithms. IT organizations should already be evaluating the use of robotic process automation (RPA) software and other technologies from a sourcing perspective, but this is also a chance to put these new automation and augmentation capabilities to the test within the organization. A powerful example from an information technology perspective would be a data or server factory. Moving IT services, migrating data or consolidating records can be extraordinarily laborious and complicated. Leveraging A.I. and RPA tools can significantly reduce workload, errors and time while empowering humans to do more meaningful, judgment-based work and handle the exceptions produced by A.I. tools.
  3. Shift the IT organization left: The concept of shifting testing left has long been standard practice in improving application development by engaging quality controls earlier in the process — moving those activities closer to the business. In the same way, M&A activity should push IT closer to the organization. Shifting IT’s operating model to more effectively capture and deliver on business demand is a common theme — but M&A both requires and provides a significant opportunity for truer IT-business integration. There is a chance to redefine traditional IT roles in ways that better align to and serve lines of business to drive revenue or improve bottom-line results. Additionally, M&A provides the opportunity to reorganize IT so that it is not focused only on traditional maintenance activities or taking orders, but instead on innovating and generating value. As M&A activities ramp up, think carefully about the optimal way to integrate internal people and skills into the business based on IT demand. This both supports the M&A activities and serves to position IT and its workforce for new opportunities in the longer term.
  4. Manage the talent life cycle and traditional competency models: A fundamental error that leaders sometimes make during M&A, particularly in IT, is focusing so much on the technological complexity of change that some core human capital elements are forgotten. In the short term, IT leaders need to address issues like attrition of key talent. By partnering with HR and business leaders to fund retention incentive packages or bring talented people into the enterprise from shared services centers (e.g. captives) or other labor sources, executives will make sure that there are clear lines of control to manage some of the volatility associated with M&A. In the longer term, there are two key actions that IT leaders should consider as an outcome of M&A integrations and divestitures. First, rethink the talent pipeline to rebrand the organization and attract new talent aligned to the goals of the new organization as well as exercise managed attrition activities to ensure the IT workforce is fit for purpose going forward. Second, refresh traditional competency models around IT strategy and service delivery to focus on digital transformation, cloud opportunities, innovation and robotics as well as aggressively reskill the workforce with investments in training and certification in new technologies and platforms.

Everyone knows that M&A is challenging, intricate work — no more so than in the IT organization of companies going through these types of activities. However, leaders would be remiss in not thinking about how to reshape their IT organizations and operating models in conjunction with a merger or acquisition. In many ways, this opportunity is a box that’s only temporarily opened — once it closes, it can be difficult to go back and exact the type of transformation needs for IT to remain relevant in a world of rapidly advancing and highly distributed technology capabilities in the enterprise.

Source: the value of the surge: How to use M&A to reshape your IT organization

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To choose the right PaaS vendor, know thyself

A critical enabler of enterprise application modernization is development automation products broadly described as platform as a service. These are public or private cloud services that build upon low-level infrastructure services and consist of a collection of middleware services. PaaS offerings target a broad spectrum of users, from traditional developers to spreadsheet jockeys and Salesforce gurus. This dichotomy makes it difficult to assess a PaaS vendor.

PaaS vendors roughly fit into two categories: the developer-focused PaaS, where products target application developers building custom applications from the ground up; and the user-focused PaaS, which adds extensible features to existing software products that enable nondevelopers to create custom applications using cloud services and back-end infrastructure. Below are popular and representative examples of each and explanations of how they can help those looking to expand their business through building cloud-native applications.

The benefit for developers in using PaaS is the reduced complexity of building cloud-native apps, since the PaaS vendor assembles and maintains most of the underlying infrastructure and toolchain. As such, PaaS can offload much of the coding and infrastructure assembly for mobile and web back-end services, as well as enable developers to focus on differentiating features and the user interface, not back-end plumbing. By removing developers from the task of infrastructure and tool management and allowing them to work at higher levels of programming abstraction, PaaS can significantly decrease the development cycle and enable Agile methodologies, such as continuous integration and delivery.

Although PaaS is often used by traditional software developers, the concept has been embraced by software vendors like Oracle, Salesforce, SAP and others to allow customization of their packaged software by business application users. A top-down PaaS vendor — one with a product that turns a sophisticated piece of packaged software into a platform for custom enterprise apps — enables users like business analysts, who aren’t experienced programmers, to develop cloud-native applications that automate business processes, build custom information dashboards or fill other application niches not targeted by packaged software.

The developer versus application-user PaaS vendor

PaaS technology that works best for developers takes care of coding and infrastructure setup to allow developers to program at higher levels and not worry about the back end. Businesses must pay attention to the supported environments and operating systems of each PaaS vendor to see what best fits their technology and processes.

Developer-focused PaaS

Apprenda is a software package for private PaaS that provides an application server and container for .NET and Java applications. It works across both Linux and Windows, although it targets Windows developers. Apprenda includes an execution environment for application components using a distributed, scalable server architecture, with a central management console for the underlying infrastructure, users and operations; a developer portal; and a host of APIs, services and database connectors. Earlier this year, Apprenda acquired Kismatic to provide Kubernetes support for containerized, cloud-native applications. Apprenda is well-suited for organizations building on-premises applications that want to exploit cloud-native technologies, such as distributed cluster management, without piecing together open source components and integrating them with existing data sources and security or user account infrastructure.

The Cloud Foundry ecosystem is an open source PaaS project that originated at Pivotal Labs — an EMC-VMware spinout — and is now governed by an independent foundation. Cloud Foundry is designed for cloud-native applications that use a microservices architecture, containers and RESTful APIs. It runs on a variety of both private and public virtualized infrastructure, including VMware, OpenStack, Amazon Web Services, Azure and Google Cloud. Notable features include service brokers for registration and application binding, service routers to direct traffic to the right component, and cloud controllers for system and application management. There is also a message bus, blob store for application code and packaged builds, an authentication engine, and various logging and monitoring components.

Cloud Foundry is the basis for many commercial products and online services from Pivotal itself, Atos, CenturyLink (AppFog), GE (Predix, targeting internet-of-things applications), Hewlett Packard Enterprise (Stackato), IBM (Bluemix) and Mendix. Each of these may bundle added components to the base Cloud Foundry release and be optimized or more tightly integrated with particular infrastructure-as-a-service (IaaS) platforms — e.g., IBM Bluemix with SoftLayer.

Google App Engine (GAE) is the PaaS companion to the Google Cloud Platform IaaS suite. Unsurprisingly, being from Google, it is designed for web applications and mobile back ends — just the things you’d expect to see on a Chromebook or Android phone. App Engine uses containers that are preconfigured with a supported runtime environment, with a choice of Java, Python, PHP or Go. Teams who specialize in languages other than these four, such as C++, should keep in mind that code will have to be refactored into one of the supported languages.

The platform includes various persistent storage services, including NoSQL, SQL and in-memory caches; automatic instance scaling and load balancing; asynchronous task queues, like a message bus; task scheduling; search –no surprise, being from Google; and tight integration with Google cloud and APIs for its other products, including use of Google accounts and its authentication engine.

Like GAE, Microsoft’s Azure App Service has extended its infrastructure stack with higher-level application services that streamline the process of building cloud-native apps. The focus of Azure App Service is mobile and web applications using .NET, Node.js JavaScript, Java, PHP or Python. Aside from web and mobile back ends, App Service can also be used for service interfaces between clients and servers, which is what Microsoft calls API or Logic Apps.

Like GAE, App Service automatically scales and load-balances client application traffic, and it’s tightly integrated with the underlying Azure IaaS services like SQL, NoSQL and object storage. But it also has connections with over 50 software-as-a-service (SaaS) products, including Office 365, Salesforce, Slack and Dropbox. Microsoft shops with seasoned Windows developers will naturally gravitate to Azure due to its use of Window infrastructure, tight integration with the Visual Studio toolchain, and support for .NET and Windows APIs.

Oracle Cloud is the umbrella term for the database giant’s as-a-service products, including applications like ERP and human capital management, along with raw infrastructure. Its hosted PaaS stack exploits Oracle’s core database technology and targets data-driven applications with services for SQL, NoSQL, big data — Hadoop and Spark — and real-time data pipelines and event streaming (Apache Kafka). Given Oracle’s Sun DNA, it’s no surprise Java is the preferred programming environment with a cloud service for development and testing; staging and production; and support for WebLogic, mobile back ends and JavaScript/Node.js. Like other PaaS technology, Oracle provides API management, infrastructure and service monitoring, log analysis and task automation.

Red Hat OpenShift, the commercial implementation of the OpenShift Origin open source project, is a container-based PaaS that combines Docker packaging and Kubernetes cluster management. It is available as a shared or dedicated managed service, or as on-premises packaged software. The platform supports applications in Java, Ruby, Node.js, Python, PHP and other languages through add-on modules that use the OpenShift API, called cartridges. It includes MongoDB, MySQL, PostgreSQL and SQLite databases, as well as JBoss middleware, including the Red Hat BPM Suite, web server, business rules management and push notification service.

PaaS is a growing market, but still dwarfed by IaaS

PaaS is the smallest of the three primary as-a-service categories, with roughly only one-third the revenue as IaaS; however, it is growing just as fast. According to the 451 Group, PaaS revenue is projected to grow 25% per year, with revenue doubling by 2020. Despite such growth, PaaS will still be a much smaller market than IaaS and dominated by the mega-cloud players like Amazon Web Services — which is adding many application-layer services — Google, IBM and Microsoft.

User-focused PaaS

Business application users must look for a PaaS vendor with a stack that extends existing application platforms like Oracle, SAP or Salesforce, since these will provide the easiest access to data, metadata, user accounts and higher-level application services to create custom dashboards, data visualizations or form-based collaborative workflows.

Selection of a PaaS product is highly dependent on an organization’s existing infrastructure, new application requirements, and the target audience of both the applications and developers.

Oracle Application Builder is a browser-based visual development environment that allows nonprogrammers to build applications by connecting prebuilt components using a GUI. Applications can use Oracle SaaS applications via REST APIs to create customized forms and dashboards. As with any PaaS stack, Application Builder is typically used to build custom extensions to existing applications, as well as integrate packaged and custom applications by sharing data or passing calling functions from one to another. App extensions are often new, custom web or mobile UIs that can be created with a drag-and-drop interface.

SAP HANA Cloud Platform is a managed service providing a set of application services that simplify development using any SAP application, including its HANA in-memory database. Typical use cases include building customized dashboards that can integrate HANA and on-premises data sources, mobile front ends and internet-of-things data analytics.

Salesforce App Cloud is the umbrella service for the Salesforce’s and Heroku development platforms. It features a GUI, drag-and-drop environment — Lightning — for quickly assembling custom apps from prebuilt components. Built upon the Salesforce customer relationship management application, App Cloud combines core infrastructure services like databases, storage and containers with platform services like a workflow engine, social collaboration, reporting and dashboards, a mobile back end and application UIs. Although developers can code microservices in Heroku DX, Node.js, Ruby, Python, Java and PHP, App Cloud targets business users without formal programming experience. Typical use cases include mobilizing existing Salesforce apps, integrating one or more Salesforce apps — such as customer-facing and employee apps — and building data analytics dashboards from Salesforce activity.

The right PaaS vendor depends on development team

Selection of a PaaS product is highly dependent on an organization’s existing infrastructure, new application requirements, and the target audience of both the applications and developers. Those using experienced developers to build greenfield, cloud-native applications should focus on the developer-centric PaaS frameworks and their associated infrastructure.

Source: choose the right PaaS vendor, know thyself

5 Best Practices for Outsourcing Cybersecurity

Data breaches are getting more sophisticated, more common, and more expensive; the average cost of a breach has reached $4 million, up 29% in the past three years. No organization, regardless of size or industry, can afford to ignore information security. The shortage of qualified cybersecurity personnel, combined with modern organizations preferring to outsource ancillary functions so they can focus on their core competencies, has resulted in many organizations choosing to outsource part or all of their cybersecurity operations, often to a managed security services provider (MSSP).

There are many benefits to outsourcing information security, including cost savings and access to a deeper knowledge base and a higher level of expertise than is available in-house. However, outsourcing is not without its pitfalls, and there are issues that organizations should be aware of when choosing a cybersecurity vendor. This article will discuss five best practices for outsourcing information security.

1. Never use an offshore cybersecurity provider

The bargain-basement prices offered by offshore cybersecurity providers are tempting to budget-conscious organizations, especially since many other IT functions, such as mobile app and software development, are routinely offshored.

However, mobile app and software development do not necessitate allowing contractors to have access to your organization’s network or sensitive data, and the work can be reviewed by an internal team before deployment. Due to the nature of the work, cybersecurity contractors have full access to your organization’s internal systems and data, in real-time. Meanwhile, there is no way to verify the education, skills, or experience levels of the offshore company’s employees, nor is there any way to ensure they have undergone comprehensive criminal background checks. Finally, if a breach occurs, you may have little or no legal recourse against the offshore provider even if you have proof that the breach was due to negligence or a malicious insider at their company.

Information security is simply too important to entrust to an offshore contractor. There is also a practical matter to consider: Offshore providers are unable to provide on-site security staff at your location, which leads into our second best practice.

2. Steer clear of providers that suggest solutions that are completely remote-based

Some cybersecurity companies provide services that are strictly remote, conducted entirely via telephone and the internet. However, a remote-only solution cannot fully protect your organization, especially since over half of all data breaches can be traced back to negligence, mistakes, or malicious acts on the part of company insiders. An MSSP can protect your organization from the outside and the inside through a hybrid solution that combines remote security operations center (SOC) monitoring with on-site security personnel who can work in tandem with your existing staff or function as a standalone, embedded SOC. These on-site personnel can help your organization establish cybersecurity policies and employee training, as well as immediately respond to security breaches.

3. Beware of providers that claim their solutions provide 100% protection against breaches

When evaluating cybersecurity vendors, you will inevitably come across providers who claim that their solutions are foolproof and will prevent all breaches. This is impossible. Cybersecurity experts are engaged in a never-ending war against hackers. As soon as one vulnerability is fixed, hackers devote themselves to finding the next one, and every new technology that is introduced presents brand-new vulnerabilities.

While a comprehensive cybersecurity solution will protect your organization against most breaches, the cold, hard reality is that there is no such thing as an impenetrable security system. Steer clear of providers who try to tell you otherwise. Not only are they being dishonest, they may also be unable to effectively respond when a breach does occur.

4. Ensure that the provider’s team has real-world experience in cybersecurity

Some cybersecurity providers hire recent college graduates or certificate-holders with plenty of classroom training in information security theory but little or no actual work experience protecting critical infrastructures. Cybersecurity expertise cannot be honed within the confines of a classroom. Entry-level trainees lack the experience to fully grasp the nuances of real-world information security procedures and challenges, which means they are far more likely to make mistakes than enterprise security professionals with years of experience. Make sure that your provider hires only seasoned security experts.

5. Beware of providers who talk about “magic hardware” and little else

Enterprise security hardware platforms are a hot topic in the information security industry right now, and many exciting new developments are being made in this area. However, security hardware is not a standalone solution, and you should be wary of any provider that tries to sell you on a “magic hardware” platform that will purportedly address all of your security needs. Security hardware is a tool for human security professionals; it does not replace them.

Outsourcing your organization’s information security is serious business. You are handing the keys to your kingdom – your company’s internal systems and sensitive data – to a third-party vendor. Asking critical questions and following best practices during the evaluation and selection process will ensure a successful, long-term relationship between your organization and your cybersecurity provider.

Source: Best Practices for Outsourcing Cybersecurity

Business Beware: Top 12 Mistakes To Avoid When Outsourcing

Outsourcing can be a blessing or a curse. Avoid these 12 mistakes to ensure your outsourced projects are successful.

From cost savings to freeing up company resources, there are many reasons why businesses choose to outsource.

Outsourcing can take mundane tasks off the plate of valuable employees, or bring expertise to an area a business doesn’t have in-house.

Whatever the reasons for outsourcing, it can be easy to make mistakes that turn into big pitfalls.

Fortunately, there are some simple steps you can take to avoid the most common outsourcing mistakes.

Here’s a look at the top 12 mistakes businesses make when outsourcing.

1. Not Starting Things Off With a Kickoff Meeting

Before you start working together, you should take the time to make sure you’re on the same page about tasks that need to be done, timelines, payments, and other key details. Making the assumption that all of your expectations, systems, and deadlines are clear could lead to some big setbacks.

2. Not Looking at Previous Work Samples Before Hiring

Whether you’re outsourcing content, graphic design, or social media, it makes sense to review the work of anyone you’re considering working with. Not only will you know that they’re capable of quality work, you’ll get a feel for their style. If it doesn’t fit with what you’re looking for, it’s better to know that before signing a contract.

3. Not Communicating While Working Together

Communication needs to keep going beyond that initial meeting; providing a detailed process, making yourself available for questions, and setting up checkpoints to make sure that the project is going in the right direction are also important steps.

This is especially crucial with complex projects, where a minor miscommunication can add up to big problems. To avoid those miscommunications, and subsequent finger pointing, it’s better to keep an open dialogue while the project is going on.

Another reason to keep the channels of communication open is that business goals can change and evolve, and if you don’t explain what the goals are while you’re working with an outsourcer, the relationship won’t be a productive one.

4. Being Slow to Pay What You Owe

A surefire way to run off talent is by leaving them hanging when it’s time to pay an invoice. It’s not good business practice overall, and it’s also not going to get you good results.

5. Trying to Collaborate Across Time Zones

There’s nothing wrong with outsourcing to a country several time zones away, but if the project requires detailed collaboration that will have one of you up in the middle of the night on a frequent basis, perhaps it’s not going to be a productive pairing.It’s also worth considering whether language and cultural differences will hinder productivity.

6. Expecting Top Notch Work for Minimum Wage Prices

Labor rates vary from country to country. But assuming you can pay the lowest price negotiable, no matter where you’re hiring from, and still expect premium work is a mistake. You will need to pay remote workers a fair compensation if you want them to do a good job.

7. Foregoing Project Management Tools

There are some great project management tools out there, including apps and websites like Trello, Slack, Google Apps and Evernote that let you chat, update information about projects, and upload documents, all without having to go back and forth in emails or missed calls.

They can be between as few as two people or scaled up to include dozens. Ignoring productivity tools like these will make effective communication a lot harder, and lead to higher frustration on both ends.

8. Losing Your Cool

Staying in touch and making it clear from the beginning what the procedure should be if a contractor needs help will minimize problems. If and when problems do arise, don’t jump to conclusions and fire off an angry instant message or email; you’re not going to get things back on track by being rude. Try to take into account factors that could have led to the problem, and clear it up with an understanding call or email.

9. Relying Solely on Written Messages to Communicate

Sometimes a topic or glitch is too complex to discuss in back-and-forth messages. Instead, talking on the phone, using video chatting, or even using screen sharing to view the file or website in question will make communication smoother and more efficient.

10. Forgetting to Reward Good Work

Almost everyone feels more motivated and connected to their work when they know they’re appreciated, and remote workers are no exception. When a freelancer or contractor has done a good job, let them know. Chances are they’ll feel more invested in the work and want to go the extra mile for you in the future.

11. Outsourcing the Wrong Things

Just because you can outsource something doesn’t necessarily mean you should. Make sure there’s value in outsourcing a project or task; if not, do it in-house.

12. Micromanaging

Just like going completely silent and expecting remote workers to intuit what you need is a mistake, too much dictating can make the outsourcing process much less efficient. Micromanaging also means the outsourcer won’t be bringing their own expertise and ideas, which you presumably hired them for, into a project.

Source: Beware: Top 12 Mistakes To Avoid When Outsourcing

Cognitive Computing Energizes the Enterprise

Today’s intelligent systems can learn from customer data to discover and provide insights that drive better experiences, heighten employee engagement, and inspire companies to innovate

Like it or not, humans and robots are increasingly being forced to coexist in the same environment, and there’s little anyone can do to prevent it.

This is becoming especially apparent in the CRM space, where customer service, sales, and marketing professionals are all starting to feel the impact of technological advancements in their respective fields. More software vendors are now offering software that incorporates artificial intelligence (AI), machine learning, deep learning, cognitive computing, chatbots, intelligent assistants, augmented reality, and a slew of other innovations, designed to perform the otherwise time-consuming tasks that needlessly tie up humans. The technologies also promise to process CRM data, along with other structured and unstructured data, more quickly to learn from behavioral patterns, discover solutions, and surface recommendations and insights to help customer-facing professionals do their jobs more efficiently and improve interactions with customers.

Though AI has been around for decades, the applications are coming out in droves. In April, an IDC forecast predicted that this year, worldwide spending on cognitive and artificial intelligence systems would increase by 59.3 percent to reach $12.5 billion. Of that amount, 9.8 percent is likely to encompass automated customer service agent tools. This suggests that these systems have really come into their own and can now be used by organizations of all sizes and experience levels.

There is a lot of buzz in the air, but the hype is not unfounded. To keep up in today’s competitive business landscape, companies that automate areas that can be simplified for the sake of a better customer experience will lead the way. And experts agree that all companies should at least be figuring out where to invest to get the ball rolling.


Michele Goetz, principal analyst at Forrester Research, says it’s important to understand what makes a solution “cognitive” or “intelligent,” and what doesn’t.

Chatbots, for example, have typically worked in a programmatic, or deterministic, fashion, meaning they can identify certain keywords or phrases that will indicate to which type of agent a case should be routed within the call center. A financial institution’s chatbot might pick up the term “loan” or “account balance” and then link that customer to the agent most qualified to help.

But “today’s intelligent chatbots—you can think of them as cognitive agents—are very different,” Goetz says. These systems, she points out, go steps beyond, as they can take voice-of-the-customer information, call center notes and recordings, facts, email info, and other components of a conversation before moving the case into recognition analysis. At this point, the assistant can understand the types of questions being asked, why they are being asked, when those interactions are taking place, and the results that they produce.

When a customer asks about his account balances, an intelligent assistant can reason that he might be interested in seeing his last five transactions. Or it might surmise that the customer could be concerned about potential fraud. Registering this, it can surface information to the agent who will take the call, or to the customer directly. “It’s much more evolutionary. It’s aware. It’s adaptive, and that’s where the intelligence comes from,” Goetz says.

This kind of functionality can be applied in many ways to benefit customers. A financial institution can use the technology to help customers with portfolio reallocation. In such scenarios, the intelligent assistant can guide the financial adviser as he works. It can observe the conversations, acting as a search mechanism that helps the adviser find the right information across different systems and resources, so he can make more intelligent recommendations faster. Instead of an agent having to write down the customer’s information during a call and get back to him in a week with a proposal, the interaction can now happen in near real time.

In many cases, all of this can happen behind the scenes, unseen by the customer. “The customer doesn’t necessarily know that this is happening,” Goetz points out. In many cases, too, the agent can train the machine, increasing the likelihood that the recommendations it makes are better and smarter over time.

Vince Jeffs, director of strategy and product marketing at Pegasystems, stresses the importance of the instant feedback loop in teaching intelligent assistants. There are plenty of cases where machines can’t quite solve these problems yet, he says, noting that some cases still have to be escalated to humans. “But they can be assisted by these machines, and the agents can guide the machine on whether or not its recommendations were good or not.”


While the buzz might signal something that is still quite a ways into the future, a reality in which many customer interactions are completed by machines is not that far off. Most companies—or at least those that don’t have astronomical budgets—shouldn’t expect a very high level of sophistication just yet. But there are many tools they can implement to save their employees time on routine tasks.

Customer support is a common area of investment, experts agree. A number of technologies can help “reduce the strain” on call centers as more people contact them for help and advice.

“If you can have an automated agent that is able to answer 50 percent to 60 percent of the questions in a timely way, provide good responses to customers, and doesn’t make them upset, that’s a win-win for everybody,” says Dave Schubmehl, a research director at IDC covering AI and cognitive systems.

Schubmehl points to Autodesk’s use of IBM Watson’s Conversation tool to develop a digital concierge as an example of a company that got it right. The agent, referred to as “Otto,” can handle 60 percent of web-based customer service inquiries. Autodesk improved support ticket resolution time by 99 percent and significantly upped customer satisfaction.

According to Schubmehl, to make it work, the company had to organize its knowledge base. The system was trained to handle common customer and partner issues, including resetting passwords or rebooting a program after it failed. More difficult cases that weren’t covered by the knowledge base could get handed off to a live agent.

“It is important we provide our customers with consistent quality paired with the shortest response and resolution time,” said Gregg Spratto, vice president of operations at Autodesk, in a statement. “Our collaboration with IBM Watson allows us to expand the Otto concierge service and deliver prompt, effective, and authentic engagement to our customers.”

Goetz mentions a similar case involving a company that sells insurance through employers and sees its heaviest traffic during open enrollment periods. In the past the firm had to train temporary staff members to handle those kinds of calls and answer questions about insurance types and policies. “The quality of customer service would significantly decrease, and it was inconsistent in terms of how customers were supported,” Goetz says. Furthermore, since they were contracted employees, the temporary agents were not as invested in their jobs as full-time employees. With IBM Watson, the company was able to move to first- and second-tier support levels when volumes peaked, pushing the more sophisticated cases to live agents.

Another major benefit of automation through intelligence is in reducing the “grunt work” and freeing people up to do more interesting work that requires more complex skill sets and critical thinking, according to Schubmehl. After all, it can get depressing just helping one customer after another recover passwords or log in to their accounts all day.

This is in line with research from an Aberdeen Group study commissioned by Inbenta, which found that companies incorporating cognitive technologies to support customer service interactions have seen an 81 percent improvement in employee engagement rates.

“There’s been a lot of conversation about virtual agents and artificial intelligence eliminating jobs, but what we actually saw is like any other tool: It’s going to be valuable in improving [agents’] performance and their interest and satisfaction while working,” says John Forrester, Inbenta’s chief marketing officer. “Instead of dealing with repetitive, boring questions that customers might have, they’d be dealing with something that’s more in-depth and engaging.”

While customer service jobs tend to have higher turnover rates than other fields, technology can help businesses with retention, as employees are likely to be more enthusiastic and eager to learn new skills and tackle greater challenges.


Behind the scenes, cognitive automation can be useful as well. Robotic process automation (RPA) can learn routine tasks that service agents repeat throughout the day.

“Robotic process automation has been around for about 15 or 20 years, but it’s primarily been rules-based, where somebody programs the rules for the agent,” Schubmehl says. “Now we’re getting to the point where you can have AI sitting there, watching the person doing the work, and then essentially developing the script automatically that the robot should act according to.”

That’s a relatively new area, but a hot one. Technology vendors like Pegasystems, WorkFusion, NICE, Automation Anywhere, and BluePrism are all adding RPA capabilities to their tool sets. “I think we’ll see that happen more frequently in 2017 and probably 2018,” Schubmehl predicts.

And while the call center might be the most obvious area and the one that most companies will want to look at first, sales professionals can also benefit from tools that simplify otherwise tedious processes, thus freeing them to tend to other parts of their jobs. Schubmehl points to Conversica as one vendor leveraging AI to simulate human voices and carry out conversations in place of sales reps, keeping leads warm so that when they turn hot again, they can be routed to a live salesperson.

In marketing, AI technologies can be used to learn customer preferences and identify the offers, images, and deals that are most likely to appeal to customers and to identify whether they should be sent via email or other channels and devices.


Goetz recommends getting started by testing and creating proofs of concepts. She says that companies in retail, consumer packaged goods, or manufacturing have a number of consumable applications from which to choose. A pilot can cost $50,000 to $100,000; implementation could range from $500,000 to $1 million, she says, but the proof of concept has already been given in many cases, leading to return on investment as high as 20 percent or 25 percent.

“Many organizations right now are doing piloting and testing and really working out to see the feasibility, and that entry point tends to be very low,” Goetz says.

Steve Laughlin, vice president and general manager of IBM’s global consumer industry division, points out that his company’s Watson APIs can be accessed via the BlueMix app development platform to help create and test programs before they are actually deployed.

Among the uses he’s seen already, some companies are turning the APIs into programs to help them understand personality insights from written texts on social media or to analyze images that customers have already seen to determine the next set of images they will likely want to see.

Staples, for one, recently used IBM Watson APIs to create a real-life Easy Button based on its plastic marketing icon. After doing design thinking with B2B customers of all sizes, the office supply superstore chain understood that there was a demand for an office assistant’s assistant to help stock up on supplies. The button responds to voice commands. “Right now, you can go into the supply room and say, ‘Hey, Easy, we need more copier paper, and by the way, it looks like we’re short on blue pens,’” Laughlin says.

“A crazy thing happened, and people started asking Easy for things that Staples doesn’t do, and it’s created a whole pipeline of new ideas for them to work with,” Laughlin adds. “So it’s become not only an innovation for them to engage with customers, but it’s become a source of ideas from customers for potentially new innovations.”

While IBM’s Watson is likely the most prominent technology in this space, other solutions, such as CognitiveScale’s Augmented Intelligence, Digital Reasoning’s Synthesis, and Narrative Science’s Quill, are emerging as competitive options.

Schubmehl suggests several other considerations when starting out. The first is getting the right stakeholders in place and then getting them all together to identify the business processes or functions that AI will replace. With that step taken, a company can set up a shortlist of service providers and discuss the potential outcomes for the application.

“One of the things that you’ve got to remember is that if you’re going to do an AI system, it’s going to be based on data, so you need to make sure that you have access to all of the data that’s necessary to drive the system,” Schubmehl adds. “A lot of organizations don’t have any real strategy about how they keep track of their knowledge, so really getting a handle on that first is something they should think about.”

Then companies need to engage subject matter experts in the design and implementation process, Schubmehl says. If you’re designing for the call center, you need to have the call center people involved in the design and the development. “If you don’t, you’re essentially asking for trouble,” because the design might not align with their needs, he cautions.

Goetz agrees that the design element is key, noting that adopting artificial intelligence calls for a desire to provide a human experience. Companies should ask, she says, “how do I reproduce, or almost replicate, what a human experience is going to be?”

“There are plenty of instances where we, as humans, still want human interaction,” Pegasystems’ Jeffs agrees.

In fact, designing an AI system, Goetz says, should mimic the process companies use to design a customer experience program.

“Sometimes they speak, so the tone, the way that they speak—is it very formal, is it conversational, and at what age groups are they speaking—those sorts of things really go into creating the best success in adopting artificial intelligence for customer engagement and customer experience,” Goetz says. “You can’t underestimate the design component that goes into that, and that’s how businesses have to approach this technology. You cannot think this is just another scoring engine or analytic engine that runs under the hood of your CRM or website or advertising platform.”

Source: destinationcrm-Cognitive Computing Energizes the Enterprise

How Start-up Businesses Can Take Advantage of Outsourcing

The first few phases of any startup are very time and management sensitive, so does outsourcing even deserve a seat at the table? Simply put, there are so many things to be done that outsourcing is indeed a viable option during these phases. It helps by taking the load off personnel and projects.

Outsourcing some projects doesn’t only free up time, but it also leaves you with more energy and in-house resources to use and assign to core business tasks. This is how startup businesses can take advantage of outsourcing.

Outsourcing and Risk Management

Running a startup includes a lot of risks. The markets are oversaturated and volatile as it is, and taking on projects you don’t have any experience with puts your young and fragile establishment at more risk. By finding an experienced and reliable outsourcing partner, you will put risk management in their hands. Avoiding risks depends on having knowledge and experience in certain aspects of the market, and this is exactly what outsourcing providers have.

Become More Competitive

Competition and Outsourcing

There is also one more important advantage of outsourcing. This one will help you level the playing field and stay competitive in your market niche, especially if there are big players offering the same or similar products or services. For instance, you can outsource customer support to a trustworthy company that handles it on a professional level. Your startup can reap the benefits of all those perks big corporations have in-house by smartly outsourcing projects to experts.

Marketing via Outsourcing

Today in the digital age of marketing, strategies have to be devised carefully, and they have to target specific audiences. They also have to be subtle. They are meant to educate the audience, but also to nurture a relationship with them. Very rarely will a startup have the kind of money needed for hiring a team of professional digital marketing experts.

This especially applies to content marketing. Instead of hiring an expensive workforce, you can outsource production and distribution. As long as you clearly communicate the message you want to send to the audience, marketing professionals will be able to help you build better relationships and generate more leads.

Speeding Things Up

While starting a new project in-house may take up to a few weeks, outsourcing it means that it will start right away. This is simply because some of the tasks may require your workers to have specific skills and knowledge that they don’t have at that moment. Instead of investing in their training and waiting for it to be completed, you can outsource the project to a respectable outsourcing firm.

Reduce Costs with Outsourcing

Hiring a new employee just because you don’t have anyone on your staff that can finish a one-time task can prove to be a very bad investment. The best practice for handling peripheral projects is to outsource them. Your hires will be able to focus on core business tasks while the outsourcing partner handles less-important projects for you.

These are just some of the outsourcing advantages startup businesses can gain. In the end, if you are unsure whether to outsource or not, just compare the expense of outsourcing versus a full-time hire, and write down how much money you will save. This may be just the incentive you need for making the right decision.

Source:  rocksdigital-How Start-up Businesses Can Take Advantage of Outsourcing

Software robots act more like humans than expected

Benevolent bots’ or software robots designed to improve articles on Wikipedia sometimes have online ‘fights’ over content that can continue for years, say scientists who warn that artificial intelligence systems may behave more like humans than expected.

‘Benevolent bots’ or software robots designed to improve articles on Wikipedia sometimes have online ‘fights’ over content that can continue for years, say scientists who warn that artificial intelligence systems may behave more like humans than expected. Editing bots on Wikipedia undo vandalism, enforce bans, check spelling, create links and import content automatically, whereas other bots (which are non-editing) can mine data, identify data or identify copyright infringements.

Researchers from the University of Oxford and the Alan Turing Institute in the UK analysed how much they disrupted Wikipedia, observing how they interacted on 13 different language editions over ten years (from 2001 to 2010). They found that bots interacted with one another, whether or not this was by design, and it led to unpredictable consequences.

Researchers said that bots are more like humans than you might expect. Bots appear to behave differently in culturally distinct online environments. The findings are a warning to those using artificial intelligence for building autonomous vehicles, cyber security systems or for managing social media.

We may have to devote more attention to bots’ diverse social life and their different cultures, researchers said.

The research found that although the online world has become an ecosystem of bots, our knowledge of how they interact with each other is still rather poor. Although bots are automatons that do not have the capacity for emotions, bot to bot interactions are unpredictable and act in distinctive ways.

Researchers found that German editions of Wikipedia had fewest conflicts between bots, with each undoing another’s edits 24 times, on average, over ten years. This shows relative efficiency, when compared with bots on the Portuguese Wikipedia edition, which undid another bot’s edits 185 times, on average, over ten years, researchers said.

Bots on English Wikipedia undid another bot’s work 105 times, on average, over ten years, three times the rate of human reverts, they said. The findings show that even simple autonomous algorithms can produce complex interactions that result in unintended consequences – ‘sterile fights’ that may continue for years, or reach deadlock in some cases.

While bots constitute a tiny proportion (0.1 per cent) of Wikipedia editors, they stand behind a significant proportion of all edits. Although such conflicts represent a small proportion of bots’ overall editorial activity, the findings are significant in highlighting their unpredictability and complexity.

“We find that bots behave differently in different cultural environments and their conflicts are also very different to the ones between human editors,” said Milena Tsvetkova, from the Oxford Internet Institute.

“This has implications not only for how we design artificial agents but also for how we study them. We need more research into the sociology of bots,” said Tsvetkova.

Source: robots act more like humans than expected